Relevance estimation and actions based thereon

ABSTRACT

Computer-based systems, methods, and articles of manufacture are disclosed. In a social network embodiment, information regarding a first user is obtained and formed into a first dataset. Conceptual spaces are selected for the first user, and the first user&#39;s location is determined in the spaces. Distances between the first user and other users and their datasets are computed in the selected conceptual spaces. Actions are taken based on the distances, such as including or excluding the other users from a friends list of the first user.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority from U.S.patent application Ser. No. 14/562,615, entitled RELEVANCE ESTIMATIONAND ACTIONS BASED THEREON, filed on Dec. 5, 2014, now allowed; whichclaims priority from U.S. patent application Ser. No. 13/658,516,entitled RELEVANCE ESTIMATION AND ACTIONS BASED THEREON, filed on Oct.23, 2012, now U.S. Pat. No. 8,930,385, issued Jan. 6, 2015; which claimspriority from U.S. provisional patent application Ser. No. 61/554,778,entitled APPARATUS, METHODS, AND ARTICLES OF MANUFACTURE FOR RELEVANCEESTIMATION AND ACTIONS BASED THEREON, filed on Nov. 2, 2011. Each of theabove-referenced patent documents is hereby incorporated by reference inits entirety as if fully set forth herein, including text, figures,claims, tables, and computer program listing appendices (if present),and all other matter therein.

This application is also related to U.S. patent application Ser. No.14/656,625, entitled RELEVANCE ESTIMATION AND ACTIONS BASED THEREON,filed Mar. 12, 2015.

FIELD

The present description relates generally to estimation/determination ofrelevance or relatedness of datasets and actions based on the resultingestimates/determinations. In selected embodiments, the presentdescription relates to social networking and virtual reality.

BACKGROUND

Estimating relatedness of various data sets is useful in manyapplications. In social networking, for example, estimating potentialinterest of a first user in another user may be useful when selectingusers to suggest as potential “friends” to the first user. It is alsouseful in selecting advertising to present (render) to the particularuser. Similarly, a virtual space can be defined so that it describes agiven number of qualities. For example, a virtual reality space can bedefined so that the distance between avatars represents similarity oftheir qualities rather than the distance in the “physical” space as itappears on a computer screen or other display. There are other uses forestimating relevance/relatedness as well.

Therefore, it is desirable to facilitate the process of estimatingrelatedness of real or notional objects, and taking action based on theresulting estimates.

SUMMARY

A need thus exists for improved ways to make estimations ofrelevance/relatedness, and to act in response to the estimations.

Embodiments of the present invention are directed to methods, apparatus,and articles of manufacture that satisfy one or more of these needs.Some described embodiments are used to define a distance in a virtualreality or a social network between objects, concepts, and/or otherdatasets.

Selected embodiments disclosed herein include a computer-implementedmethod. In accordance with the method, a first dataset is formed frominformation submitted for a first entity. The method also includesselecting one or more conceptual spaces for the first entity. The methodadditionally includes determining one or more locations of the firstentity in the one or more conceptual spaces, at least one location ofthe one or more locations of the first entity per each conceptual spaceof the one or more conceptual spaces. The method further includescomputing one or more distances from the first entity to a second entityin the one or more conceptual spaces, at least one distance of the oneor more distances per conceptual space of the one or more conceptualspaces, the second entity having a second dataset formed based oninformation regarding the second entity. The method further includestesting each distance of the one or more distances under one or morecriteria. The method further includes rendering (e.g., displaying)information regarding relatedness of the first entity and the secondentity, in response to the step of testing.

These and other features and aspects of the present invention will bebetter understood with reference to the following description, drawings,and appended claims.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates selected components of a computer-based systemconfigured to perform steps of a method in accordance with an embodimentdescribed in this document;

FIG. 2 illustrates selected steps of a method for making estimations ofrelevance/relatedness and acting based on the estimations, in accordancewith an embodiment disclosed in this document;

FIG. 3 illustrates selected aspects of a two-dimensional conceptualspace; and

FIG. 4 illustrates selected aspects of a conceptual distance in atwo-dimensional conceptual space.

DETAILED DESCRIPTION

In this document, the words “embodiment,” “variant,” “example,” andsimilar expressions refer to particular apparatus, process, or articleof manufacture, and not necessarily to the same apparatus, process, orarticle of manufacture. Thus, “one embodiment” (or a similar expression)used in one place or context can refer to a particular apparatus,process, or article of manufacture; the same or a similar expression ina different place can refer to a different apparatus, process, orarticle of manufacture. The expression “alternative embodiment” andsimilar expressions and phrases are used to indicate one of a number ofdifferent possible embodiments. The number of possible embodiments isnot necessarily limited to two or any other quantity. Characterizationof an item as “exemplary” means that the item is used as an example.Such characterization of an embodiment does not necessarily mean thatthe embodiment is a preferred embodiment; the embodiment may but neednot be a currently preferred embodiment. The embodiments are describedfor illustration purposes and are not necessarily strictly limiting.

The words “couple,” “connect,” and similar expressions with theirinflectional morphemes do not necessarily import an immediate or directconnection, but include connections through mediate elements withintheir meaning, unless otherwise specified or inherently required.

“Contiguous” and its inflectional morphemes are used to describe adimension that is not entirely discrete; the dimension may be entirelycontinuous (i.e., the value of the dimension's variable is allowed toassume any number), or continuous in one or more intervals (i.e., thevalue of the dimension's variable is allowed to assume any number withinthe one or more intervals). A contiguous dimension may have one or morediscrete values in addition to the one or more continuous intervals. Acontiguous interval may be bounded on one side only, or bounded on oneside and unbounded on the other side.

Other and further definitions and clarifications of definitions may befound throughout this document.

A “user” may be a person, a business entity, or another organization.

A “dataset” is a collection of information about a particular user,avatar, place, event, business transaction, or actual/potentialoccurrence. For a person, generation of a dataset is described below. Anadvertisement may have a dataset associated with it, including, forexample, descriptions of a product or service, and demographic data andpersonality traits of those person who are likely to be interested inone or more of the products/services marketed through the advertisement.

Reference will now be made in detail to several embodiments andaccompanying drawings. Same reference numerals are used in the drawingsand the description to refer to the same apparatus elements and methodsteps. The drawings are in simplified form, not to scale, and omitapparatus elements and method steps that can be added to the describedapparatus and methods, while including certain optional elements andsteps.

In a physical world, all objects, including people, exist in physicalspace and have specific place in the physical space. A distance betweentwo objects is calculated as the distance in the space between twopoints corresponding to the respective positions of the objects. In athree-dimensional Euclidean space, for example, the distance ΔS betweentwo points A(x1, y1, z1) and B(x2, y2, z2) is defined as a solution tothe following equation:

ΔS ² =Δx ² +Δy ² +Δz ²,   (1.1)

where ΔS=S_(A)−S_(b); Δx=x₁−x₂; Δy=y₁−y₂; and Δz=z₁−z₂. In a moregeneral non-Euclidian metric space, the geometry is defined by themetric

ds²=g_(ij)dx^(i)dx_(j),   (1.2)

where g_(ik) is a metric tensor. In a more general non-Euclidian metricspace, the distance between point A and point B is defined as

$\begin{matrix}{S = {{\int_{A}^{B}\ {s}} = {\int_{A}^{B}{\left( {g_{ij}\ {x^{i}}{x^{j}}} \right)^{\frac{1}{2}}.}}}} & (1.3)\end{matrix}$

A mathematical concept of space need not be confined to the notion ofphysical space. In mechanics, for example, so called configurationalspace for N particles is defined as 3N-dimensional space, whichrepresents all possible positions of all particles. A phase space is aspace, in which all possible states of a system are represented, whereineach possible state corresponds to a unique point in the phase space. Inmechanics, a phase space may consist of all possible positions andmomentum values of all particles. A phase space for N particles isdefined as 6N-dimensional space wherein each particle has sixcoordinates: three spacial coordinates—x₁, x₂, x₃, and three componentsof momentum—p₁, p₂, p₃.

An abstract mathematical space can be used to describe any number ofvariables. For example, a logical variable P that can have only twovalues, True or False, can be described as a 1-dimensional discretespace consisting of two points: x₁=True and x₂=False. In such space, wecan define the distance S between two logical values P and Q as S=0, ifA=B, and S=∞, if B=Ā (not A). In other works, two concepts occupy thesame space in a conceptual space if they are identical, or they areinfinitely far from each other if they are opposites. Such conceptualspace need not be discrete. A contiguous space may represent a conceptthat changes contiguously. In such space the more two concepts are alikeor similar, the closer they are in their conceptual space. The moredissimilar they are, the further apart they are from each other in sucha space.

Let us define, for example, a two-dimensional conceptual space (x, y)occupied by objects having two qualities—X and Y—wherein dimension xrepresents the quality of X and dimension y represents the quality Y, asshown in FIG. 3. If the conceptual space a “musical” space, the pair ofqualities X and Y may represent, respectively, frequency (measured inHertz, for example) and volume (measured in Decibels, for example). Twonotes of the same frequency and the same volume will occupy the samepoint in this “musical” space. The “distance” ΔS between note A and noteB may be defined as a solution to ΔS from the following equation:

ΔS ² =Δx ² +Δy ²,   (1.4)

where Δx=x₂−x₁ is the difference in frequency and Δy=y₂−y₁ is thedifference in volume. See FIG. 4.

In a “political” space, for example, the qualities X and Y may representdifferent political views. Say, X may represent views about fiscalpolicy and Y may represent views about social policies. Two peoplehaving the same views on both issues will occupy the same point in such“political” space. The distance between political views of two peoplemay be calculated as before, from the equation ΔS²=Δx²+Δy².

We can similarly build a 3-D “color” space, where the three coordinates(x, y, z) represent three primary colors, say x for red, y for green andz for yellow. Two points in this space will coincide if they have thesame color ingredients, i.e., they will have the same color.

A conceptual space may have any number of dimensions representing agiven number of qualities that objects occupying such space possess.Such spaces may be contiguous or discrete. Indeed, such spaces can becontiguous in one or more dimensions, and discrete in one or more otherdimensions.

A simple topological space usually has dimensions represented by anatural number. Thus, a well-familiar physical space has dimension 3.The surface of a sphere has a dimension of 2. The Minkowski spacetime ofSpecial Theory of Relativity has a dimension of 4. The phase space inmechanics may have a dimension of 6N. In mathematics, there are spacesdefined with dimensions which are represented by fractional numbers. AHousdorf dimension does not have to be whole. One can interpret such adimension having a whole part n and a fractional part p as a space whosedimension is uncertain and may be n or n+1 with the probability 0≦p≦1.

Virtual spaces known today attempt to emulate physical reality. Virtualworlds are 2-D or 3-D virtual spaces that look like physical worldswherein avatars and other objects can move around in a virtual space.Online games allow players to build virtual worlds, such as a virtualstore or a virtual city. Virtual space, however, is essentially aconceptual space and can represent conceptual reality, rather thanmerely mimicking physical reality, however whimsical. A virtual spacecan be defined so that it describes a given number of qualities. Forexample, a virtual reality space can be defined so that the distancebetween two avatars represents similarity of their qualities rather thanthe distance in the simulated “physical” space as it appears on acomputer screen or other display.

Social networks currently known may define rigid groups (such as FaceBook or LinkedIn) or circles (Google+) to which each member of thenetwork either belongs or does not belong. A social network, however,can be constructed using a conceptual space wherein the “distance” inthe network between people is defined by their relationship and theaffinity of their views and interests. For example, a multi-dimensionalconceptual space can be defined wherein one dimension is familyrelationship (so that close relatives will be closer to each other thandistant relatives); another dimension can represent professionaloccupation (so that the closer the professional occupations of twopeople are, the closer the two people will appear in this dimension ofthe conceptual space); yet another dimension can represent politicalviews (the closer the political views of members, the closer they are inthe political dimension of this space); still another dimension canrepresent age; yet another discrete dimension can represent gender;still another dimension can represent religious views; yet anotherdimension can represent the frequency of communications between members;other dimensions can represent various hobbies, interests, and otheraffinities. The physical proximity can be one of the dimensions in suchconceptual space. The degree of separation (such as in LinkedIn,immediate friends—1^(st) degree, friends of friends—2d degree, etc.) canbe another discrete dimension in such conceptual space. In such space,people with similar views, occupation, hobbies, age, etc., may occupylocations in close proximity to each other, thereby creating aconceptual neighborhood.

People tend to socialize with their family members, neighbors,classmates, colleagues, relatives, friends, people of the samenationality and/or religion, and people with similar views andinterests. A social network constructed as a conceptual space groupspeople together by their closeness in all such categories, or in asubset of the categories. Such social network can represent the realhuman interaction in its full spectrum and richness much more naturalthan existing social networks.

Any member who likes another member can “drag” this member closeroverriding the distance calculated by the algorithm based on a given setof characteristics of these two members. One member can bring one membercloser or push the member further, overriding the calculated positions.This can be done, for example, visually by dragging an avatar of themember in a virtual space to or from one's own position, or, forexample, by selecting a member of the social network and clicking on“Like” or “Dislike” (or similar) buttons and specifying the degree ofsimilarity/attraction or dissimilarity/repulsion towards that person ona given scale.

The distance between network members can be used as a proxy for thedegree of influence of one member on another and used, for example, inselecting advertisements for the one member.

FIG. 1 is a simplified block diagram representation of a computer-basedexemplary system 100 configured in accordance with selected aspects ofthe present description. As shown in FIG. 1, the system 100 is coupledto user computers 180 via a communication network 190. FIG. 1 does notshow many hardware and software modules, and omits several physical andlogical connections. The system 100 can be implemented as a specialpurpose data processor, a general-purpose computer, a computer system,or a group of networked computers or computer systems configured toperform the steps of methods described throughout this document, eitheralone or in conjunction with other elements such as the user computers180. In some embodiments, the system 100 is built on a personal computerplatform, such as a Wintel PC or a Mac computer. The personal computermay be a desktop or a notebook computer.

The system 100 includes a processor 110, read only memory (ROM) module120, random access memory (RAM) module 130, network interface 140, amass storage device 150, and a database 160. These components arecoupled together by a bus 115. In the illustrated embodiment, theprocessor 110 is a microprocessor, and the mass storage device 150 is amagnetic disk drive. The mass storage device 150 and each of the memorymodules 120 and 130 are connected to the processor 110 to allow theprocessor 110 to write data into and read data from these storage andmemory devices. The network interface 140 couples the processor 110 tothe network 190, for example, the Internet. The nature of the network190 and of the devices that may be interposed between the system 100 andthe network 190 determine the kind of network interface 140 used in thesystem 100. In some embodiments, for example, the network interface 140is an Ethernet interface that connects the system 100 to a local areanetwork, which, in turn, connects to the Internet.

The database 160 is used for organizing and storing data that may beneeded or desired in performing the method steps described in thisdocument. The database 160 may be a physically separate system coupledto the processor 110, as illustrated in FIG. 1. In one alternativeembodiment, the processor 110 and the mass storage device 150 areconfigured to perform the functions of the database 160.

The processor 110 reads and executes program code instructions stored inthe ROM module 120. Under control of the program code, the processor 110configures the system 100 to perform all or some of the steps of themethods described below. In addition to the ROM module 120, the programcode instructions may be embodied in machine-readable storage media,such as hard drives, floppy diskettes, CD-ROMs, DVDs, and similardevices that can store the instructions permanently or temporarily, in anon-transitory manner. The program code can also be transmitted over atransmission medium, for example, over electrical wiring or cabling,through optical fiber, wirelessly, or by any other form of physicaltransmission. The transmission can take place over a dedicated linkbetween telecommunication devices, or through a wide- or local-areanetwork, such as the Internet, an intranet, extranet, or any other kindof public or private network. In one embodiment, the program code isdownloaded to the system 100 through the network interface 140.

The system 100 may implement a social network server or a virtualreality server.

FIG. 2 is a process flow diagram illustrating selected steps anddecision blocks of a process 200 for determining the degree of potentialinterest of a first user in a second user. The users may be at theirrespective user computers 180. The process 200 may be performed, inwhole or in part, by the system 100 of FIG. 1, for example, incommunication with the user computers 180. The process 200 may also beperformed by various other systems.

Although the process steps and decisions are described serially, certainsteps and decisions may be performed by separate elements in conjunctionor in parallel, asynchronously or synchronously, in a pipelined manner,or otherwise. There is no particular requirement that the steps anddecisions be performed in the same order in which this description liststhem, except where a specific order is inherently required, explicitlyindicated, or is otherwise made clear from the context. Furthermore, notevery illustrated step and decision block may be required in everyembodiment in accordance with the invention, while some steps anddecision blocks that have not been specifically illustrated, may bedesirable or necessary in some embodiments in accordance with theinvention.

At flow point 201, the system 100 is properly configured and ready toperform the steps of the process 200. In step 205, the system registersa user, generating a corresponding dataset. (In variants, the systemobtains a dataset of a different entity, and the steps below are thesame or substantially the same mutatis mutandis as applicable to suchentity.) The user may be at one of the user computers 180 connected tothe system 100 via the network 190, as is illustrated in FIG. 1.Registration entails, for example, obtaining the user's name, postaladdress, email address, twitter handle, telephone number, anddemographic information (e.g., age, gender, physical location, income,race), and personality profile (e.g., interests, hobbies, likes,dislikes, self-evaluation of the user's personality), communicationdevice ownership and communication device preferences. The user may begiven an option to attach relative weights to the user's differentinterests, hobbies, likes, dislikes, and other attributes. Questionsdesigned to elicit information for determining a psychological profileof the user may also be offered to the user. Legally mandated ordesirable disclosures, disclaimers, and terms of the agreement betweenthe operator of the system 100 and the user may also be presented to theuser at the time of registration. The disclosures and disclaimers mayvary depending on the physical site of the user, in order to tailorthese documents to the requirements of the user's country, state, oranother jurisdiction. The responses received from the user and recordsindicating which documents have been provided to the user may be storedin the database 160.

In step 210, the user's “location” is determined in one or moreconceptual spaces; the dimensions of each of the conceptual spacescorrespond to all or some of the data provided by the user, which mayinclude the weights the user assigned to one or more of the dimensions(interests, hobbies, etc.); the weights can be applied linearly or insome other fashion. When applied linearly, a weight of w can double thedistance (say Δx) in the dimension to which the weight corresponds. Theweights may be assigned by the users explicitly (e.g., 1, 2, 3, etc.),through a translation algorithm applied to words signifying importanceor affection (e.g., “don't care much”=0.5, “neutral”=1, “important”=2,“very important”=3, “can't live without”=4, etc.), based on dataregarding the user's internet activity on the site of the system 100 orother sites (previous searches, web pages visited, links selected,advertisements responded to, etc.), or through application of results ofa psychological profile applied to answers provided by the user duringregistration or otherwise. The psychological profile may be a knownprofile, such as a Myers-Briggs Type Indicator profile or a WilsonLearning Systems profile, or another possibly custom profilingtechnique. Additional dimensions may also be selected and weighted basedon other data, for example, based on data regarding the user's internetactivity on the site of the system 100 or other sites (previoussearches, web pages visited, links selected, advertisements respondedto, etc.), or through application of results of the psychologicalprofile applied to answers provided by the user during registration orotherwise.

It should be noted that some conceptual spaces may not have a dimensioncorresponding to a particular data point provided by the user; and theuser may not have provided data for one or more dimensions of aparticular conceptual space. When a conceptual space has one or moredimensions in which the user's coordinate is not known, the user's“location” in such a space may be considered to correspond to thecoordinates in the other (known) dimensions, ignoring the unknowncoordinates.

In step 215, one or more of the conceptual spaces in which the distanceshave been determined are selected for the user. Note that this step mayin fact precede the step 210, so that the conceptual spaces are selectedbefore the locations are determined.

In step 220, the user's distance(s) from one or more other users (ormore generally from other datasets) is (or are, as the case may be)computed in one or more of the conceptual spaces. Distance computationmay be static or dynamic. When dynamically computed, the distance in aparticular conceptual space may depend on dynamically changinginformation, such as the user's internet activity (searches etc.),internet activity of another user (or other dataset) to which thedistance is computed, and on other dynamically varying conditions. Thedynamically varying conditions may include financial data (stock marketsindices, bond markets indices, individual security quotations, commodityindices, individual commodities, real estate indices, volatilityindicators, released survey data, and similar financial data), breakingnews items, weather (local or at predetermined locations), sports eventsand competition results, price changes of consumer products/services,geographic location of the user (which may be dynamically obtainablefrom the user's portable communication device), geographic location ofanother user or another entity, and similar live or recent data.

It should be noted that the distance between two users may be the samein both directions or perspectives. The distance may also vary dependingon the perspective. For example, the distance between a first use and asecond user measured from the first user's perspective, may differ fromthe distance between the user's measured from the second user'sperspective.

In decision block 225, a distance of the user/dataset from anotheruser/dataset is evaluated using (tested against) one or more criteria.If the one or more criteria are met, an action is performed in step 230.

In the social network context, for example, a simple criteria may be afirst limit value: if the distance is not greater than the first limitvalue, for example, the distance meets the criteria, and theuser/dataset from which perspective the distance was measured is offeredthe other user/dataset as a potential object for inclusion in the firstuser's group of interest, such as a group of the first user's friends.The second user may also be automatically included in the group if thecriteria are met. Similarly, an advertisement may be targeted to thefirst user if the distance from the first user's perspective meets oneor more criteria, or if the distance from the advertisement's datasetperspective meets the one or more criteria.

More than one distance may be evaluated/tested in the decision block225, and more than one set of criteria may be used in the decision block225.

The one or more criteria by which a distance is evaluated may depend (1)on a particular user/dataset from whose perspective the distance ismeasured, (2) on the other user/dataset (to whom the distance ismeasured), and/or (3) other parameters, such as time and businessnecessity.

After the step 230, the process may terminate at flow point 299. If theone or more criteria are not met in the decision block 225, the processmay terminate at the flow point 299 without performing the action in thestep 230. The process may then be repeated for other users or for thesame user, for example, when information is dynamically updated.

This document describes in detail the inventive apparatus, methods, andarticles of manufacture for making estimations/determinations ofrelevance, relatedness, and/or affinity of datasets and actions based onthe resulting estimates/determinations. This was done for illustrationpurposes. Neither the specific embodiments of the invention as a whole,nor those of its features necessarily limit the general principlesunderlying the invention. The specific features described herein may beused in some embodiments, but not in others, without departure from thespirit and scope of the invention as set forth herein. Various physicalarrangements of components and various step sequences also fall withinthe intended scope of the invention. Many additional modifications areintended in the foregoing disclosure, and it will be appreciated bythose of ordinary skill in the art that in some instances some featuresof the invention will be employed in the absence of a corresponding useof other features. The illustrative examples therefore do not define themetes and bounds of the invention and the legal protection afforded theinvention, which function is carried out by current and future claimsand their equivalents.

I claim:
 1. An article of manufacture comprising non-volatile machine-readable storage medium with machine-executable program code stored in the medium, the program code comprising instructions for group management of a social network, the social network having a first entity and a plurality of other entities, the first entity and each entity of the plurality of other entities being associated with a plurality of characteristics, the instructions implementing steps comprising: first assigning a value to each characteristic of the plurality of characteristics of the first entity to obtain a first dataset associated with the first entity, the first dataset comprising the values of the plurality of characteristics of the first entity, wherein the values of at least some of the characteristics of the first entity are based at least in part on information regarding the characteristics of the first entity provided by the first entity; second assigning a value to each characteristic of the plurality of characteristics of said each entity of the plurality of other entities to obtain a plurality of datasets associated with the plurality of other entities, a dataset of the plurality of datasets per entity of the plurality of other entities, each dataset of the plurality of datasets comprising the values of the characteristics of the entity with which said each dataset is associated, wherein the values of at least some of the characteristics of said each entity of the plurality of other entities are based at least in part on information regarding the characteristics provided by said each entity of the plurality of other entities; computing for said each entity of the plurality of other entities a distance in a first conceptual space between a point in the first conceptual space defined by the first dataset, and a point in the first conceptual space defined by values of the characteristics of the dataset of said each entity of the plurality of other entities, thereby obtaining a plurality of distances in the first conceptual space, a distance of the plurality of distances corresponding to a different entity of the plurality of other entities, wherein each dimension of the first conceptual space corresponds to a different one of said plurality of characteristics, and wherein distances along at least one of the dimensions in the first conceptual space are weighted by a weight assigned by the first entity to characteristic associated with said at least one of the dimensions; and selecting entities for a first group from the plurality of other entities so that distance in the first conceptual space corresponding to each entity included in the first group is within a first predetermined distance limit.
 2. The article of manufacture according to claim 1, wherein the instructions further implement steps comprising: at least one step selected from transmitting to the first entity information about the entities of the first group for inclusion in one or more affinity groups of the first entity, and transmitting to the entities of the first group information about the first entity for inclusion in affinity groups of the entities of the first group.
 3. The article of manufacture according to claim 2, wherein: the instructions are such that the step of first assigning comprises receiving over one or more computer communication networks from the first entity first data from which at least part of the first dataset can be determined, and the step of second assigning comprises receiving over the one or more computer communication networks from a second entity of the plurality of other entities second data from which at least part of the dataset associated with the second entity can be determined; and the one or more computer communication networks comprise a cellular communication network.
 4. The article of manufacture according to claim 2, wherein: the instructions implement the step of first assigning so that the step of first assigning comprises receiving over a computer network from the first entity first data from which at least part of the first dataset can be determined, and the step of second assigning comprises receiving over the computer network from a second entity of the plurality of other entities second data from which at least part of dataset associated with the second entity can be determined; and the computer network comprises the Internet.
 5. The article of manufacture according to claim 4, wherein the first conceptual space is a non-Euclidean metric conceptual space.
 6. The article of manufacture according to claim 4, wherein the first conceptual space is a Euclidean conceptual space.
 7. The article of manufacture according to claim 4, wherein at least one of said plurality of characteristics is a physical characteristic.
 8. The article of manufacture according to claim 4, wherein at least one of said plurality of characteristics is a psychological profile characteristic.
 9. The article of manufacture according to claim 4, wherein the first conceptual space comprises at least one discrete dimension.
 10. The article of manufacture according to claim 4, wherein the first conceptual space comprises at least one contiguous dimension comprising a plurality of intervals.
 11. The article of manufacture according to claim 4, wherein the instructions further implement: computing a second perspective distance for a third entity of the plurality of other entities, by calculating a distance in a second conceptual space between a point in the second conceptual space defined by the first dataset of the first entity, and a point in the second conceptual space defined by dataset corresponding to the third entity of the plurality of other entities, wherein the second conceptual space is defined so that each dimension of the second conceptual space corresponds to a different one of said plurality of characteristics, and wherein distances along at least one of the dimensions in the second conceptual space are weighted by a weight assigned to characteristic associated with said at least one of the dimensions in the second conceptual space by the third entity; and including in a second group the first entity if the second perspective distance is within a second predetermined distance limit in the second conceptual space, the second group being associated with the third entity.
 12. The article of manufacture according to claim 4, wherein the instructions are such that at least one of the step of first assigning and the step of second assigning comprises applying a translation algorithm to words signifying importance or affection.
 13. The article of manufacture according to claim 4, wherein at least one of the step of first assigning and the step of second assigning is performed at least in part dynamically.
 14. A method of providing information to a user about one or more entities of a plurality of entities through an apparatus comprising at least one computing device coupled to a computer network, the user being associated with a plurality of characteristics, and each entity of the plurality of entities being associated with the plurality of characteristics, the method comprising: assigning by the apparatus a value to each characteristic of the user and said each entity of the plurality of entities to obtain a first dataset associated with the user, the first dataset comprising the values of the characteristics of the user, and a plurality of datasets of the plurality of entities, a dataset of the plurality of datasets per entity of the plurality of entities, each dataset of the plurality of datasets comprising the values of the characteristics of the entity with which said each dataset is associated, the step of assigning being performed for the user based at least on information regarding the characteristics obtained from the user, the step of assigning being performed for said each entity of the plurality of entities based at least on information regarding the characteristics provided about said each entity of the plurality of entities; computing by the apparatus for said each entity of the plurality of entities a distance in a first conceptual space between a point in the first conceptual space defined by the first dataset, and a point in the first conceptual space defined by values of the characteristics of the dataset of said each entity of the plurality of other entities, thereby obtaining a plurality of distances in the first conceptual space, a distance of the plurality of distances corresponding to a different entity of the plurality of entities, wherein each dimension of the first conceptual space corresponds to a different characteristic of the plurality of characteristics, and wherein distances along at least one of the dimensions in the first conceptual space are weighted by a weight assigned to characteristic associated with said at least one of the dimensions; selecting by the apparatus one or more entities for a first group from the plurality of entities so that distance in the first conceptual space corresponding to each entity included in the first group is within a first predetermined distance limit; and providing information of potential interest to the user about the one or more entities of the first group.
 15. The method of claim 14, wherein the steps of assigning and computing are performed at least in part based on dynamically changing information.
 16. The method of claim 15, wherein the dynamically changing information comprises Internet activity of the user.
 17. The method of claim 15, wherein the dynamically changing information comprises Internet activity of a person other than the user.
 18. The method of claim 15, wherein the dynamically changing information comprises one or more news items.
 19. The method of claim 15, wherein the dynamically changing information comprises financial information.
 20. The method of claim 15, wherein the information of potential interest to the user comprises an advertisement.
 21. A computing apparatus for providing information to a user about one or more entities of a plurality of entities, the user being associated with a plurality of characteristics, and each entity of the plurality of entities being associated with the plurality of characteristics, the apparatus comprising: at least one processor; program code storage storing machine-executable instructions, the program code storage being coupled to the at least one processor to enable the processor to read the machine-executable instructions; and a network interface coupling the at least one processor to a computer network; wherein, when the at least one processor executes the machine-executable instructions, the at least one processor causes the computing apparatus to: assign a value to each characteristic of the plurality of characteristics of the user and to each characteristic of the plurality of characteristics of said each entity of the plurality of entities to obtain a first dataset associated with the user, the first dataset comprising the values of the characteristics of the user, and a plurality of datasets of the plurality of entities, a dataset of the plurality of datasets per entity of the plurality of entities, each dataset of the plurality of datasets comprising the values of the characteristics of the entity with which said each dataset is associated, wherein the values of the first dataset are assigned based at least in part on information regarding the characteristics obtained from the user, and wherein the values in said each dataset of the plurality of datasets are assigned based at least in part on information regarding the characteristics provided about the entity of the plurality of entities associated with said each dataset; compute for said each entity of the plurality of entities a distance in a first conceptual space between (1) a point in the first conceptual space defined by the first dataset, and (2) a point in the first conceptual space defined by values of the characteristics of the dataset of said each entity of the plurality of other entities, thereby obtaining a plurality of distances in the first conceptual space, a distance of the plurality of distances corresponding to a different entity of the plurality of entities, wherein each dimension of the first conceptual space corresponds to a different characteristic of the plurality of characteristics, and wherein distances along at least one of the dimensions in the first conceptual space are weighted by a weight assigned to characteristic associated with said at least one of the dimensions; select by the apparatus one or more entities for a first group from the plurality of entities so that distance in the first conceptual space corresponding to each entity included in the first group is within a first predetermined distance limit; and provide information of potential interest to the user about the one or more entities of the first group.
 22. The computing apparatus of claim 21, wherein, when the at least one processor executes the machine-executable instructions, the at least one processor causes the computing apparatus to assign the values and compute the distances at least in part based on dynamically changing information.
 23. The computing apparatus of claim 22, wherein the dynamically changing information comprises Internet activity of the user.
 24. The computing apparatus of claim 22, wherein the dynamically changing information comprises Internet activity of a person other than the user.
 25. The computing apparatus of claim 22, wherein the dynamically changing information comprises one or more news items.
 26. The computing apparatus of claim 22, wherein the dynamically changing information comprises financial information.
 27. The computing apparatus of claim 22, wherein the information of potential interest to the user comprises an advertisement. 